A short report on different socio-economic parameters in different districts of Bihar.


Problem Statement

Elections are round the corner in the Indian State of Bihar. One of the clients of SocialCops wants to understand the level of development in different socio-economic parameters across all the districts of Bihar. Using open data, prepare an index that measures and ranks districts of Bihar on socio-economic parameters. Also prepare a short report on the output describing each of the components of the Index.


Solution

Open data from various sources was collected, tidied up, processed and compiled into file bihar_sample.xlsx using R and Excel. After intense research and thought, 10 socio-economic parameters were selected and most of them were converted from an absolute value to percentage values in the above file. Now using R these parameters are indexed and plotted for a better understanding of socio-economic situation in different districts of Bihar.

1.Sex Ratio

The Sex ratio is the ratio of males to females in a population. The ideal sex ratio is 1:1. Due to selective terminations of pregnancy, and female infanticide this ratio has been disbalanced. Hence Government and society as a whole puts tremendous importance on the revival of a healty sex ratio and it is one of the key socio-economic paramter. Here three subparameters Sex Ratio at Birth, Sex Ratio (0-4 Years) and Sex Ratio (All Ages) are also considered. The scale is number of females per 1000 males.

First we load the required libraries xlsx and ggplot2 along with the excel file into the environment.

library(xlsx)
library(ggplot2)
bihar <- read.xlsx("bihar_sample.xlsx", sheetIndex = 1)

Now we plot the line graph, code for which is given below.

options(width = 100)
ggplot(bihar, aes(State...District,group = 1)) + 
    geom_line(aes(y = Sex.Ratio.at.Birth, colour = "Sex.Ratio.at.Birth")) + 
    geom_line(aes(y = Sex.Ratio..0..4.Years., colour = "Sex.Ratio..0..4.Years.")) + 
    geom_line(aes(y = Sex.Ratio..All.Ages., colour = "Sex.Ratio..All.Ages.")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=923, colour = 'Mean Sex.Ratio.at.Birth')) + geom_hline(aes(yintercept=925, colour = 'Mean Sex.Ratio..0..4.Years.')) + geom_hline(aes(yintercept=951, colour = 'Mean Sex.Ratio..All.Ages.')) + geom_point(aes(y = Sex.Ratio.at.Birth, colour = "Sex.Ratio.at.Birth")) + geom_point(aes(y = Sex.Ratio..0..4.Years., colour = "Sex.Ratio..0..4.Years.")) + geom_point(aes(y = Sex.Ratio..All.Ages., colour = "Sex.Ratio..All.Ages.")) + ggtitle("Sex Ratio at Birth") + 
    theme(plot.title = element_text(face="bold"))

alt text

To order different districts of Bihar on Sex Ratio we run following code. Here the districts are ranked as per their Sex Ratio [Higher is better].

sex_ratio <- bihar[ order(-bihar[,2]), ]
sex_ratio[,1:4]
##      State...District Sex.Ratio.at.Birth Sex.Ratio..0..4.Years. Sex.Ratio..All.Ages.
## 8               Buxar                997                    960                  983
## 3          Aurangabad                985                    968                 1001
## 17         Kishanganj                984                    988                 1060
## 4               Banka                978                    982                  979
## 25 Pashchim Champaran                971                    985                  882
## 10               Gaya                970                    973                 1034
## 6           Bhagalpur                961                    919                  904
## 37             Supaul                959                    971                  950
## 5           Begusarai                958                    953                  975
## 12              Jamui                956                    952                  988
## 36              Siwan                945                    948                  961
## 19          Madhepura                938                    950                  916
## 34            Sheohar                937                    917                  939
## 13          Jehanabad                936                    924                 1000
## 23            Nalanda                933                    945                 1003
## 18         Lakhisarai                930                    909                  946
## 21             Munger                930                    917                  920
## 32              Saran                930                    923                 1005
## 30            Saharsa                929                    920                  928
## 1               Bihar                923                    925                  951
## 7             Bhojpur                923                    917                  998
## 16           Khagaria                922                    938                  897
## 15            Katihar                920                    928                  976
## 26              Patna                915                    921                  925
## 29             Rohtas                913                    917                  989
## 2              Araria                911                    931                  927
## 20          Madhubani                910                    901                  926
## 24             Nawada                909                    952                 1050
## 33         Sheikhpura                901                    930                 1014
## 11          Gopalganj                899                    902                  948
## 27    Purba Champaran                896                    908                  898
## 38           Vaishali                892                    896                  957
## 31         Samastipur                890                    884                  924
## 9           Darbhanga                886                    893                  923
## 22        Muzaffarpur                883                    896                  911
## 35          Sitamarhi                882                    872                  944
## 28             Purnia                878                    878                  938
## 14    Kaimur (Bhabua)                871                    908                  954

2.Literacy Rate

Literacy Rate is the total percentage of the population of an area at a particular time aged seven years or above who can read and write with understanding. Here the denominator is the population aged seven years or more. Literacy rate is also a key factor that determines the development status of an area or district. Despite government programmes, Bihar’s literacy rate increased only “sluggishly”. One of the main factors contributing to this relatively low literacy rate is the lack of proper school facilities as well as the sheer inefficiency of teaching staff across the government run education sector. There is a shortage of classrooms to accommodate all the students. In addition, there is no proper sanitation in most schools. Here two subparameters are also considered Literacy Rate (Male) and Literacy Rate (Female). The scale is in percentage.

Now we plot the line graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1)) + 
    geom_line(aes(y = Literacy.Rate..Total., colour = "Literacy.Rate..Total.")) + 
    geom_line(aes(y = Literacy.Rate..Male., colour = "Literacy.Rate..Male.")) + 
    geom_line(aes(y = Literacy.Rate..Female., colour = "Literacy.Rate..Female.")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=67.5, colour = 'Mean Literacy.Rate..Total')) + geom_hline(aes(yintercept=77.1, colour = 'Mean Literacy.Rate..Male.')) + geom_hline(aes(yintercept=57.6, colour = 'Mean Literacy.Rate..Female.')) + ggtitle("Literacy Rate") + geom_point(aes(y = Literacy.Rate..Total., colour = "Literacy.Rate..Total.")) + geom_point(aes(y = Literacy.Rate..Male., colour = "Literacy.Rate..Male.")) + geom_point(aes(y = Literacy.Rate..Female., colour = "Literacy.Rate..Female.")) + 
    theme(plot.title = element_text(face="bold"))

alt text

As we can see in the plot there is a wide gender disparity in the literacy rate in Bihar. To order different districts of Bihar on literacy rate we run following code. Here the districts are ranked as per their Literacy Rate [Higher is better].

literacy_rate <- bihar[ order(-bihar[,5]), ]
literacy_rate[,c(1,5:7)]
##      State...District Literacy.Rate..Total. Literacy.Rate..Male. Literacy.Rate..Female.
## 26              Patna                  78.3                 86.5                   69.3
## 29             Rohtas                  76.5                 86.8                   66.4
## 21             Munger                  76.1                 84.6                   67.2
## 14    Kaimur (Bhabua)                  75.7                 85.7                   65.4
## 7             Bhojpur                  74.3                 86.7                   62.0
## 8               Buxar                  73.5                 84.8                   62.3
## 36              Siwan                  73.1                 81.6                   64.2
## 5           Begusarai                  72.6                 81.2                   64.2
## 3          Aurangabad                  72.2                 83.1                   61.7
## 38           Vaishali                  71.4                 80.2                   62.3
## 6           Bhagalpur                  71.0                 79.1                   62.1
## 32              Saran                  70.9                 81.2                   60.9
## 10               Gaya                  69.8                 80.3                   60.0
## 13          Jehanabad                  69.7                 81.6                   57.9
## 11          Gopalganj                  68.6                 78.7                   58.4
## 33         Sheikhpura                  68.3                 79.6                   57.5
## 18         Lakhisarai                  68.1                 78.4                   57.7
## 22        Muzaffarpur                  67.7                 74.9                   59.8
## 1               Bihar                  67.5                 77.1                   57.6
## 23            Nalanda                  67.5                 78.6                   56.8
## 16           Khagaria                  67.0                 75.0                   58.0
## 4               Banka                  65.4                 76.7                   54.5
## 31         Samastipur                  65.2                 74.7                   54.8
## 30            Saharsa                  64.4                 75.7                   52.3
## 37             Supaul                  64.0                 75.2                   52.5
## 19          Madhepura                  63.2                 74.3                   51.3
## 24             Nawada                  63.2                 74.6                   53.1
## 12              Jamui                  62.4                 74.8                   50.3
## 2              Araria                  61.9                 71.6                   51.7
## 17         Kishanganj                  61.5                 70.5                   53.4
## 25 Pashchim Champaran                  61.4                 71.4                   49.8
## 27    Purba Champaran                  61.4                 71.0                   50.9
## 20          Madhubani                  61.2                 71.4                   50.6
## 35          Sitamarhi                  61.2                 69.9                   52.1
## 9           Darbhanga                  61.0                 70.2                   51.4
## 15            Katihar                  60.0                 67.9                   52.3
## 34            Sheohar                  59.8                 69.5                   49.6
## 28             Purnia                  58.4                 65.2                   51.2

3.Children currently attending school

The percentage of children currrently attending school should be as high as possible because it will promote skill development and knowledge among the children. A social parameter it reflects the presence of proper schooling infrastructure, willingness of parents to send their wards to school and general level of development in the district.

Two sub parameters have also been studied here Children currently attending school (Age 6-17 years)(Male) & Children currently attending school (Age 6-17 years)(Female) . The age of children in this dataset is 6-17 years and the scale is in percentage.

Now to analyse this social parameter we will plot the line graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1)) + 
    geom_line(aes(y = Children.currently.attending.school..Age.6.17.years......Total., colour = "Children.currently.attending.school..Age.6.17.years......Total.")) + 
    geom_line(aes(y = Children.currently.attending.school..Age.6.17.years......Male., colour = "Children.currently.attending.school..Age.6.17.years......Male.")) + 
    geom_line(aes(y = FemalChildren.currently.attending.school..Age.6.17.years......Female., colour = "FemalChildren.currently.attending.school..Age.6.17.years......Female.")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=92.2, colour = 'Mean Children.currently.attending.school..Age.6.17.years......Total.')) + geom_hline(aes(yintercept=92.7, colour = 'Mean Children.currently.attending.school..Age.6.17.years......Male.')) + geom_hline(aes(yintercept=91.6, colour = 'Mean FemalChildren.currently.attending.school..Age.6.17.years......Female.')) + ggtitle("Children currently attending school (Age 6-17 years)") + geom_point(aes(y = Children.currently.attending.school..Age.6.17.years......Total., colour = "Children.currently.attending.school..Age.6.17.years......Total.")) + geom_point(aes(y = Children.currently.attending.school..Age.6.17.years......Male., colour = "Children.currently.attending.school..Age.6.17.years......Male.")) + geom_point(aes(y = FemalChildren.currently.attending.school..Age.6.17.years......Female., colour = "FemalChildren.currently.attending.school..Age.6.17.years......Female.")) + 
    theme(plot.title = element_text(face="bold")) + theme(legend.position = "bottom", legend.direction = "vertical")

alt text

Now to order different districts of Bihar on percentage of Children currently attending school we run following code. Here the districts are ranked as per their respective percentage [Higher is better].

children_school <- bihar[ order(-bihar[,8]), ]
literacy_rate[,c(1,8:10)]
##      State...District Children.currently.attending.school..Age.6.17.years......Total.
## 26              Patna                                                            91.6
## 29             Rohtas                                                            94.1
## 21             Munger                                                            91.9
## 14    Kaimur (Bhabua)                                                            94.7
## 7             Bhojpur                                                            92.0
## 8               Buxar                                                            92.3
## 36              Siwan                                                            96.5
## 5           Begusarai                                                            92.2
## 3          Aurangabad                                                            93.9
## 38           Vaishali                                                            94.3
## 6           Bhagalpur                                                            90.8
## 32              Saran                                                            94.2
## 10               Gaya                                                            88.7
## 13          Jehanabad                                                            93.5
## 11          Gopalganj                                                            96.0
## 33         Sheikhpura                                                            90.0
## 18         Lakhisarai                                                            91.1
## 22        Muzaffarpur                                                            94.6
## 1               Bihar                                                            92.2
## 23            Nalanda                                                            90.3
## 16           Khagaria                                                            93.2
## 4               Banka                                                            90.5
## 31         Samastipur                                                            96.6
## 30            Saharsa                                                            88.7
## 37             Supaul                                                            92.3
## 19          Madhepura                                                            91.9
## 24             Nawada                                                            90.7
## 12              Jamui                                                            88.6
## 2              Araria                                                            90.4
## 17         Kishanganj                                                            88.3
## 25 Pashchim Champaran                                                            92.9
## 27    Purba Champaran                                                            90.3
## 20          Madhubani                                                            92.4
## 35          Sitamarhi                                                            90.8
## 9           Darbhanga                                                            92.3
## 15            Katihar                                                            89.9
## 34            Sheohar                                                            90.2
## 28             Purnia                                                            91.6
##    Children.currently.attending.school..Age.6.17.years......Male.
## 26                                                           91.8
## 29                                                           94.0
## 21                                                           92.1
## 14                                                           95.5
## 7                                                            93.9
## 8                                                            93.2
## 36                                                           96.4
## 5                                                            92.0
## 3                                                            93.8
## 38                                                           93.8
## 6                                                            91.0
## 32                                                           94.5
## 10                                                           91.5
## 13                                                           94.3
## 11                                                           96.2
## 33                                                           91.3
## 18                                                           92.2
## 22                                                           94.5
## 1                                                            92.7
## 23                                                           91.8
## 16                                                           93.2
## 4                                                            91.7
## 31                                                           96.4
## 30                                                           90.2
## 37                                                           93.6
## 19                                                           92.9
## 24                                                           92.7
## 12                                                           91.3
## 2                                                            91.2
## 17                                                           88.9
## 25                                                           93.5
## 27                                                           90.2
## 20                                                           93.1
## 35                                                           90.6
## 9                                                            93.1
## 15                                                           90.2
## 34                                                           90.8
## 28                                                           91.9
##    FemalChildren.currently.attending.school..Age.6.17.years......Female.
## 26                                                                  91.4
## 29                                                                  94.2
## 21                                                                  91.7
## 14                                                                  93.9
## 7                                                                   90.0
## 8                                                                   91.3
## 36                                                                  96.6
## 5                                                                   92.4
## 3                                                                   94.0
## 38                                                                  94.9
## 6                                                                   90.5
## 32                                                                  93.8
## 10                                                                  85.8
## 13                                                                  92.7
## 11                                                                  95.8
## 33                                                                  88.7
## 18                                                                  89.8
## 22                                                                  94.8
## 1                                                                   91.6
## 23                                                                  88.8
## 16                                                                  93.1
## 4                                                                   89.3
## 31                                                                  96.8
## 30                                                                  87.1
## 37                                                                  90.8
## 19                                                                  90.8
## 24                                                                  88.7
## 12                                                                  85.7
## 2                                                                   89.6
## 17                                                                  87.6
## 25                                                                  92.2
## 27                                                                  90.4
## 20                                                                  91.6
## 35                                                                  91.1
## 9                                                                   91.4
## 15                                                                  89.6
## 34                                                                  89.6
## 28                                                                  91.3

4.Children engaged in work

The percentage of children engaged in work also refferd to as Child Labour refers to the employment of children in any work that deprives children of their childhood, interferes with their ability to attend regular school, and that is mentally, physically, socially or morally dangerous and harmful. This practice is considered exploitative by many international organisations.Poverty is the greatest single cause behind child labour. This socio-economic parameter gives a holistic view of povert and scope of development in the district or state. Subparameters Children aged 5-14 years engaged in work (%)(Total)(Male) & Children aged 5-14 years engaged in work (%)(Total)(Female) have also been considerd. The age of children in this dataset is 5-14 years and the scale is in percentage.

Now to analyse this social parameter we will plot the line graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1)) + 
    geom_line(aes(y = Children.aged.5.14.years.engaged.in.work.....Total., colour = "Children.aged.5.14.years.engaged.in.work.....Total.")) + 
    geom_line(aes(y = Children.aged.5.14.years.engaged.in.work.....Total..Male., colour = "Children.aged.5.14.years.engaged.in.work.....Total..Male.")) + 
    geom_line(aes(y = Children.aged.5.14.years.engaged.in.work.....Total..Female., colour = "Children.aged.5.14.years.engaged.in.work.....Total..Female.")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=3, colour = 'Mean Children.aged.5.14.years.engaged.in.work.....Total.')) + geom_hline(aes(yintercept=3.5, colour = 'Mean Children.aged.5.14.years.engaged.in.work.....Total..Male.')) + geom_hline(aes(yintercept=2.6, colour = 'Mean Children.aged.5.14.years.engaged.in.work.....Total..Female.')) + ggtitle("Children aged 5-14 years engaged in work (Total)") + geom_point(aes(y = Children.aged.5.14.years.engaged.in.work.....Total., colour = "Children.aged.5.14.years.engaged.in.work.....Total.")) + geom_point(aes(y = Children.aged.5.14.years.engaged.in.work.....Total..Male., colour = "Children.aged.5.14.years.engaged.in.work.....Total..Male.")) + geom_point(aes(y = Children.aged.5.14.years.engaged.in.work.....Total..Female., colour = "Children.aged.5.14.years.engaged.in.work.....Total..Female.")) + 
    theme(plot.title = element_text(face="bold")) + theme(legend.position = "bottom", legend.direction = "vertical")

alt text

Now to order different districts of Bihar on percentage of Children engaged in work we run following code. Here the districts are ranked as per their respective percentage [Lower is better].

children_work <- bihar[ order(bihar[,11]), ]
children_work[,c(1,11:13)]
##      State...District Children.aged.5.14.years.engaged.in.work.....Total.
## 29             Rohtas                                                 1.1
## 14    Kaimur (Bhabua)                                                 1.2
## 3          Aurangabad                                                 1.4
## 8               Buxar                                                 1.4
## 7             Bhojpur                                                 1.6
## 13          Jehanabad                                                 1.6
## 18         Lakhisarai                                                 1.8
## 5           Begusarai                                                 1.9
## 21             Munger                                                 1.9
## 36              Siwan                                                 1.9
## 12              Jamui                                                 2.0
## 38           Vaishali                                                 2.0
## 26              Patna                                                 2.1
## 27    Purba Champaran                                                 2.2
## 6           Bhagalpur                                                 2.3
## 4               Banka                                                 2.4
## 11          Gopalganj                                                 2.4
## 16           Khagaria                                                 2.4
## 25 Pashchim Champaran                                                 2.4
## 10               Gaya                                                 2.5
## 31         Samastipur                                                 2.7
## 24             Nawada                                                 2.8
## 30            Saharsa                                                 2.9
## 1               Bihar                                                 3.0
## 19          Madhepura                                                 3.0
## 22        Muzaffarpur                                                 3.0
## 35          Sitamarhi                                                 3.1
## 20          Madhubani                                                 3.3
## 32              Saran                                                 3.4
## 33         Sheikhpura                                                 3.7
## 37             Supaul                                                 3.8
## 15            Katihar                                                 4.0
## 23            Nalanda                                                 4.3
## 34            Sheohar                                                 4.6
## 2              Araria                                                 4.9
## 9           Darbhanga                                                 6.7
## 28             Purnia                                                 8.7
## 17         Kishanganj                                                 9.1
##    Children.aged.5.14.years.engaged.in.work.....Total..Male.
## 29                                                       1.5
## 14                                                       1.7
## 3                                                        1.8
## 8                                                        2.1
## 7                                                        2.1
## 13                                                       2.1
## 18                                                       2.3
## 5                                                        2.6
## 21                                                       2.5
## 36                                                       2.3
## 12                                                       2.3
## 38                                                       2.4
## 26                                                       3.0
## 27                                                       2.5
## 6                                                        2.8
## 4                                                        3.5
## 11                                                       2.9
## 16                                                       3.1
## 25                                                       3.1
## 10                                                       3.4
## 31                                                       3.1
## 24                                                       3.1
## 30                                                       3.2
## 1                                                        3.5
## 19                                                       2.8
## 22                                                       3.4
## 35                                                       3.4
## 20                                                       4.0
## 32                                                       3.5
## 33                                                       3.8
## 37                                                       3.7
## 15                                                       4.9
## 23                                                       4.2
## 34                                                       4.6
## 2                                                        5.1
## 9                                                        6.2
## 28                                                       9.5
## 17                                                       9.5
##    Children.aged.5.14.years.engaged.in.work.....Total..Female.
## 29                                                         0.7
## 14                                                         0.6
## 3                                                          0.8
## 8                                                          0.7
## 7                                                          1.0
## 13                                                         1.1
## 18                                                         1.3
## 5                                                          1.1
## 21                                                         1.3
## 36                                                         1.4
## 12                                                         1.7
## 38                                                         1.5
## 26                                                         1.1
## 27                                                         1.8
## 6                                                          1.8
## 4                                                          1.2
## 11                                                         1.9
## 16                                                         1.7
## 25                                                         1.5
## 10                                                         1.6
## 31                                                         2.3
## 24                                                         2.6
## 30                                                         2.5
## 1                                                          2.6
## 19                                                         3.2
## 22                                                         2.7
## 35                                                         2.7
## 20                                                         2.6
## 32                                                         3.2
## 33                                                         3.6
## 37                                                         4.0
## 15                                                         3.1
## 23                                                         4.3
## 34                                                         4.7
## 2                                                          4.7
## 9                                                          7.3
## 28                                                         7.9
## 17                                                         8.7

5.Prevlance of any type of disablity

Disability is the consequence of an impairment that may be physical, cognitive, mental, sensory, emotional, developmental, or some combination of these. A disability may be present from birth, or occur during a person’s lifetime. Individuals may also qualify as disabled if they have had an impairment in the past or are seen as disabled based on a personal or group standard or norm. Such impairments may include physical, sensory, and cognitive or developmental disabilities. Mental disorders (also known as psychiatric or psychosocial disability) and various types of chronic disease may also qualify as disabilities. Prevlance of disablity is an important socio-economic parameter that reflects the status of health infrastructure, level of nutrition and number of able bodied men & women.

Subparameters Prevalence of any type of Disability (Total)(Male) & Prevalence of any type of Disability (Total)(Female) have also been examined. The scale here is number per 100,000 population.

Now to analyse this social parameter we will plot the line graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1)) + 
    geom_line(aes(y = Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total., colour = "Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total.")) + 
    geom_line(aes(y = Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Male., colour = "Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Male.")) + 
    geom_line(aes(y = Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Female., colour = "Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Female.")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=1617, colour = 'Mean Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total.')) + geom_hline(aes(yintercept=1958, colour = 'Mean Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Male.')) + geom_hline(aes(yintercept=1262, colour = 'Mean Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Female.')) + ggtitle("Prevalence of any type of Disability (Per 100,000 Population)(Total)") + geom_point(aes(y = Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total., colour = "Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total.")) + geom_point(aes(y = Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Male., colour = "Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Male.")) + geom_point(aes(y = Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Female., colour = "Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Female.")) + 
    theme(plot.title = element_text(face="bold")) + theme(legend.position = "bottom", legend.direction = "vertical")

alt text

Now to order different districts of Bihar on prevlance of any type of disablity we run following code. Here the districts are ranked as per their prevlance of any type of disablity. [Lower is better].

disablity <- bihar[ order(bihar[,14]), ]
disablity[,c(1,14:16)]
##      State...District Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total.
## 36              Siwan                                                                 1167
## 19          Madhepura                                                                 1234
## 17         Kishanganj                                                                 1236
## 26              Patna                                                                 1255
## 12              Jamui                                                                 1417
## 8               Buxar                                                                 1428
## 7             Bhojpur                                                                 1435
## 38           Vaishali                                                                 1437
## 4               Banka                                                                 1443
## 27    Purba Champaran                                                                 1472
## 37             Supaul                                                                 1475
## 14    Kaimur (Bhabua)                                                                 1492
## 5           Begusarai                                                                 1493
## 21             Munger                                                                 1530
## 29             Rohtas                                                                 1556
## 18         Lakhisarai                                                                 1589
## 22        Muzaffarpur                                                                 1591
## 28             Purnia                                                                 1601
## 32              Saran                                                                 1603
## 1               Bihar                                                                 1617
## 35          Sitamarhi                                                                 1622
## 11          Gopalganj                                                                 1630
## 30            Saharsa                                                                 1648
## 2              Araria                                                                 1660
## 31         Samastipur                                                                 1687
## 15            Katihar                                                                 1708
## 6           Bhagalpur                                                                 1728
## 34            Sheohar                                                                 1752
## 33         Sheikhpura                                                                 1835
## 25 Pashchim Champaran                                                                 1836
## 13          Jehanabad                                                                 1838
## 9           Darbhanga                                                                 1880
## 20          Madhubani                                                                 1914
## 16           Khagaria                                                                 1942
## 10               Gaya                                                                 1985
## 24             Nawada                                                                 1996
## 23            Nalanda                                                                 2177
## 3          Aurangabad                                                                 2223
##    Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Male.
## 36                                                                       1406
## 19                                                                       1442
## 17                                                                       1386
## 26                                                                       1513
## 12                                                                       1737
## 8                                                                        1688
## 7                                                                        1791
## 38                                                                       1726
## 4                                                                        1859
## 27                                                                       1762
## 37                                                                       1768
## 14                                                                       1850
## 5                                                                        1788
## 21                                                                       1929
## 29                                                                       1895
## 18                                                                       2001
## 22                                                                       1864
## 28                                                                       1945
## 32                                                                       2066
## 1                                                                        1958
## 35                                                                       1879
## 11                                                                       1920
## 30                                                                       1983
## 2                                                                        1952
## 31                                                                       2089
## 15                                                                       2106
## 6                                                                        2087
## 34                                                                       2196
## 33                                                                       2204
## 25                                                                       2220
## 13                                                                       2281
## 9                                                                        2358
## 20                                                                       2271
## 16                                                                       2341
## 10                                                                       2422
## 24                                                                       2483
## 23                                                                       2679
## 3                                                                        2647
##    Prevalence.of.any.type.of.Disability..Per.100.000.Population..Total..Female.
## 36                                                                          919
## 19                                                                         1013
## 17                                                                         1099
## 26                                                                          970
## 12                                                                         1101
## 8                                                                          1168
## 7                                                                          1077
## 38                                                                         1137
## 4                                                                          1035
## 27                                                                         1156
## 37                                                                         1171
## 14                                                                         1122
## 5                                                                          1201
## 21                                                                         1107
## 29                                                                         1217
## 18                                                                         1166
## 22                                                                         1289
## 28                                                                         1236
## 32                                                                         1143
## 1                                                                          1262
## 35                                                                         1348
## 11                                                                         1331
## 30                                                                         1289
## 2                                                                          1353
## 31                                                                         1247
## 15                                                                         1315
## 6                                                                          1335
## 34                                                                         1283
## 33                                                                         1475
## 25                                                                         1404
## 13                                                                         1392
## 9                                                                          1377
## 20                                                                         1537
## 16                                                                         1501
## 10                                                                         1567
## 24                                                                         1549
## 23                                                                         1684
## 3                                                                          1809

6.Children Immunized

Immunization is the process whereby a person is made immune or resistant to an infectious disease, typically by the administration of a vaccine. Vaccines stimulate the body’s own immune system to protect the person against subsequent infection or disease. This socio-economic parameter refers to the percentage of children who have been administered a vaccine. Immunization is important as it prevents us from getting sick and makes us resistant from several diseases. Proper immunization leads to a healty workforce and human capital in future while reflecting upon the present healthcare infrastructure and its reach. The age of children in this dataset is 12-23 months and the scale is in percentage.

Now to analyse this social parameter we will plot the bar graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1,y = Children.Immunized..Age.12.23.months....,label= Children.Immunized..Age.12.23.months....)) + 
    geom_bar(stat = "identity", aes(y = Children.Immunized..Age.12.23.months...., colour = "Children.Immunized..Age.12.23.months....")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=65.6, colour = 'Mean Children Immunized')) + ggtitle("Children Immunized (Age 12-23 months)(%)") + 
    theme(plot.title = element_text(face="bold")) + theme(legend.position = "bottom", legend.direction = "vertical")+ geom_text(size = 3, colour = 'white', vjust=2)

alt text

Now to order different districts of Bihar on percentage of Children Immunized we run following code. Here the districts are ranked as per their respective percentage. [Higher is better].

immunization <- bihar[ order(-bihar[,17]), ]
immunization[,c(1,17)]
##      State...District Children.Immunized..Age.12.23.months....
## 20          Madhubani                                     82.8
## 28             Purnia                                     79.9
## 16           Khagaria                                     79.4
## 32              Saran                                     76.4
## 21             Munger                                     75.9
## 31         Samastipur                                     75.8
## 13          Jehanabad                                     74.9
## 33         Sheikhpura                                     74.9
## 3          Aurangabad                                     74.2
## 36              Siwan                                     73.3
## 19          Madhepura                                     73.2
## 4               Banka                                     71.7
## 26              Patna                                     71.5
## 14    Kaimur (Bhabua)                                     71.0
## 37             Supaul                                     70.5
## 38           Vaishali                                     70.3
## 11          Gopalganj                                     69.8
## 34            Sheohar                                     68.5
## 30            Saharsa                                     68.1
## 23            Nalanda                                     67.1
## 7             Bhojpur                                     67.0
## 29             Rohtas                                     66.9
## 6           Bhagalpur                                     65.7
## 1               Bihar                                     65.6
## 8               Buxar                                     64.7
## 35          Sitamarhi                                     64.4
## 24             Nawada                                     63.1
## 18         Lakhisarai                                     62.9
## 15            Katihar                                     62.3
## 5           Begusarai                                     62.0
## 10               Gaya                                     61.7
## 9           Darbhanga                                     60.4
## 22        Muzaffarpur                                     58.0
## 2              Araria                                     54.5
## 25 Pashchim Champaran                                     52.2
## 27    Purba Champaran                                     42.0
## 12              Jamui                                     38.8
## 17         Kishanganj                                     26.6

7.Crude Death Rate

Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. It is also know as mortality rate. The crude death rate depends on the age (and gender) specific mortality rates and the age (and gender) distribution of the population. The number of deaths per 1,000 people can be higher in developed nations than in less-developed countries, despite a higher life expectancy in developed countries due to better standards of health. This happens because developed countries typically have a much higher proportion of older people, due to both lower birth rates and lower mortality rates. This socio-economic parameter is also important because it reflects the state of healtcare infrastructure and the growth rate of population in a region or district. Here the scale is number per 1000 population.

Now to analyse this social parameter we will plot the bar graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1,y = Crude.Death.Rate..Per.1000.population.,label= Crude.Death.Rate..Per.1000.population.)) + 
    geom_bar(stat = "identity", aes(y = Crude.Death.Rate..Per.1000.population., colour = "Crude.Death.Rate..Per.1000.population.")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=7, colour = 'Mean Crude Death Rate (Per 1000 population')) + ggtitle("Crude Death Rate (Per 1000 population)") + 
    theme(plot.title = element_text(face="bold")) + theme(legend.position = "bottom", legend.direction = "vertical")+ geom_text(size = 3, colour = 'white', vjust=2)

alt text

Now to order different districts of Bihar on the basis of crude death rate we run following code. Here the districts are ranked as per their crude death rate. [Lower is better].

crude <- bihar[ order(bihar[,18]), ]
crude[,c(1,18)]
##      State...District Crude.Death.Rate..Per.1000.population.
## 26              Patna                                    5.0
## 6           Bhagalpur                                    5.2
## 7             Bhojpur                                    5.5
## 14    Kaimur (Bhabua)                                    5.5
## 4               Banka                                    5.7
## 24             Nawada                                    5.7
## 13          Jehanabad                                    5.8
## 3          Aurangabad                                    6.0
## 5           Begusarai                                    6.2
## 12              Jamui                                    6.2
## 18         Lakhisarai                                    6.2
## 11          Gopalganj                                    6.3
## 8               Buxar                                    6.4
## 15            Katihar                                    6.4
## 17         Kishanganj                                    6.4
## 21             Munger                                    6.4
## 37             Supaul                                    6.4
## 29             Rohtas                                    6.6
## 31         Samastipur                                    6.7
## 1               Bihar                                    7.0
## 10               Gaya                                    7.0
## 28             Purnia                                    7.1
## 19          Madhepura                                    7.2
## 20          Madhubani                                    7.2
## 36              Siwan                                    7.3
## 38           Vaishali                                    7.4
## 34            Sheohar                                    7.5
## 2              Araria                                    7.6
## 30            Saharsa                                    7.6
## 32              Saran                                    7.6
## 23            Nalanda                                    7.7
## 27    Purba Champaran                                    7.8
## 33         Sheikhpura                                    7.8
## 9           Darbhanga                                    8.6
## 22        Muzaffarpur                                    8.6
## 25 Pashchim Champaran                                    8.7
## 16           Khagaria                                    9.3
## 35          Sitamarhi                                    9.3

8.Infant Mortality Rate

Infant mortality is the death of a child less than one year of age. It is measured as infant mortality rate (IMR), which is the number of deaths of children under one year of age per 1000 live births.The leading causes of infant mortality are birth asphyxia, pneumonia, pre-term birth complications, diarrhoea, malaria, measles and malnutrition. Many factors contribute to infant mortality such as the mother’s level of education, environmental conditions, and political and medical infrastructure. Improving sanitation, access to clean drinking water, immunization against infectious diseases, and other public health measures could help reduce high rates of infant mortality.Here the scale is number per 1000 population.

Now to analyse this social parameter we will plot the bar graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1,y = Infant.Mortality.Rate..Per.1000.Population.,label= Infant.Mortality.Rate..Per.1000.Population.)) + 
    geom_bar(stat = "identity", aes(y = Infant.Mortality.Rate..Per.1000.Population., colour = "Infant.Mortality.Rate..Per.1000.Population.")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=52, colour = 'Mean Infant.Mortality.Rate..Per.1000.Population.')) + ggtitle("Infant Mortality Rate (Per 1000 Population)") + 
    theme(plot.title = element_text(face="bold")) + theme(legend.position = "bottom", legend.direction = "vertical")+ geom_text(size = 3, colour = 'white', vjust=2)

alt text

Now to order different districts of Bihar on the basis of infant mortality rate we run following code. Here the districts are ranked as per their infant mortality rate. [Lower is better].

infant <- bihar[ order(bihar[,19]), ]
infant[,c(1,19)]
##      State...District Infant.Mortality.Rate..Per.1000.Population.
## 26              Patna                                          37
## 5           Begusarai                                          43
## 3          Aurangabad                                          44
## 7             Bhojpur                                          44
## 4               Banka                                          45
## 36              Siwan                                          46
## 24             Nawada                                          47
## 34            Sheohar                                          47
## 38           Vaishali                                          47
## 9           Darbhanga                                          48
## 11          Gopalganj                                          48
## 21             Munger                                          48
## 23            Nalanda                                          49
## 29             Rohtas                                          49
## 18         Lakhisarai                                          50
## 6           Bhagalpur                                          51
## 13          Jehanabad                                          51
## 32              Saran                                          51
## 1               Bihar                                          52
## 10               Gaya                                          52
## 20          Madhubani                                          52
## 31         Samastipur                                          52
## 8               Buxar                                          53
## 14    Kaimur (Bhabua)                                          53
## 25 Pashchim Champaran                                          53
## 27    Purba Champaran                                          53
## 12              Jamui                                          54
## 15            Katihar                                          55
## 2              Araria                                          56
## 33         Sheikhpura                                          56
## 22        Muzaffarpur                                          57
## 17         Kishanganj                                          58
## 28             Purnia                                          58
## 30            Saharsa                                          59
## 37             Supaul                                          61
## 16           Khagaria                                          63
## 35          Sitamarhi                                          64
## 19          Madhepura                                          68

9.Household Members Seeking Work

Household members seeking work may refer to simply the unemploymeny percentage in the district or state. It is a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force. Causes for this vary from unionization, bureaucratic work rules, minimum wage laws and taxes. It is a very important socio-economic parameter as it essentially tells the shape of local economy, ease of doing work, structure of taxes and the general economic growth. This parameter is of paramount importance for accessing the economic condition of a region or district.

Further study of three more sub-parameters was done which were Household members seeking work(%) (2),Household members seeking work(%) (3) & Household members seeking work(%) (4+). The scale here is in percentage.

Now to analyse this social parameter we will plot the line graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1)) + 
    geom_line(aes(y = Household.members.seeking.work.....1., colour = "Household.members.seeking.work.....1.")) + 
    geom_line(aes(y = Household.members.seeking.work.....2., colour = "Household.members.seeking.work.....2.")) + 
    geom_line(aes(y = Household.members.seeking.work.....3., colour = "Household.members.seeking.work.....3.")) + 
    geom_line(aes(y = Household.members.seeking.work.....4.., colour = "Household.members.seeking.work.....4..")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=19.26, colour = 'Mean Household.members.seeking.work.....1.')) + geom_hline(aes(yintercept=9.68, colour = 'Mean Household.members.seeking.work.....2.')) + geom_hline(aes(yintercept=3.18, colour = 'Mean Household.members.seeking.work.....3.')) + geom_hline(aes(yintercept=2.58, colour = 'Mean Household.members.seeking.work.....4..')) + ggtitle("Number of Household Members Seeking Work(%)" ) + geom_point(aes(y = Household.members.seeking.work.....1., colour = "Household.members.seeking.work.....1.")) + geom_point(aes(y = Household.members.seeking.work.....2., colour = "Household.members.seeking.work.....2.")) + geom_point(aes(y = Household.members.seeking.work.....3., colour = "Household.members.seeking.work.....3.")) + geom_point(aes(y = Household.members.seeking.work.....4.., colour = "Household.members.seeking.work.....4..")) + theme(legend.position = "bottom", legend.direction = "vertical")

alt text

Now to order different districts of Bihar on prevlance of any type of unemployment/number of household seeking work we run following code. Here the districts are ranked as per their respective percentage. [Lower is better].

household <- bihar[ order(bihar[,21]), ]
household[,c(1,21:24)]
##      State...District Household.members.seeking.work.....1. Household.members.seeking.work.....2.
## 17         Kishanganj                                 14.19                                  5.20
## 23            Nalanda                                 15.55                                  8.69
## 24             Nawada                                 15.58                                  9.04
## 33         Sheikhpura                                 15.60                                  9.12
## 13          Jehanabad                                 15.93                                  8.96
## 12              Jamui                                 16.21                                  9.00
## 10               Gaya                                 16.57                                 10.32
## 2              Araria                                 16.88                                  7.96
## 26              Patna                                 17.23                                  8.92
## 27    Purba Champaran                                 17.67                                  7.64
## 15            Katihar                                 17.84                                  8.23
## 28             Purnia                                 17.95                                  8.37
## 19          Madhepura                                 18.17                                 10.90
## 35          Sitamarhi                                 18.17                                  6.95
## 20          Madhubani                                 18.47                                  8.55
## 8               Buxar                                 18.67                                 10.01
## 7             Bhojpur                                 18.78                                 10.17
## 14    Kaimur (Bhabua)                                 19.04                                 10.61
## 34            Sheohar                                 19.11                                  7.29
## 3          Aurangabad                                 19.22                                 10.96
## 1               Bihar                                 19.26                                  9.68
## 18         Lakhisarai                                 19.31                                 11.15
## 31         Samastipur                                 19.71                                  9.01
## 38           Vaishali                                 19.71                                  8.74
## 37             Supaul                                 19.72                                 11.00
## 25 Pashchim Champaran                                 19.92                                 11.16
## 29             Rohtas                                 20.10                                 11.10
## 4               Banka                                 20.71                                 12.50
## 11          Gopalganj                                 20.94                                  9.87
## 30            Saharsa                                 21.13                                 11.68
## 32              Saran                                 21.31                                 10.22
## 5           Begusarai                                 21.47                                 10.35
## 9           Darbhanga                                 21.63                                  9.72
## 36              Siwan                                 22.55                                 10.90
## 16           Khagaria                                 22.77                                 12.09
## 22        Muzaffarpur                                 23.13                                 11.30
## 6           Bhagalpur                                 23.26                                 13.66
## 21             Munger                                 24.08                                 12.55
##    Household.members.seeking.work.....3. Household.members.seeking.work.....4..
## 17                                  1.70                                   1.06
## 23                                  3.20                                   3.14
## 24                                  3.59                                   3.90
## 33                                  3.46                                   3.59
## 13                                  3.39                                   3.38
## 12                                  2.98                                   2.82
## 10                                  3.96                                   4.53
## 2                                   2.16                                   1.42
## 26                                  3.69                                   3.18
## 27                                  2.29                                   1.55
## 15                                  2.38                                   1.52
## 28                                  2.54                                   1.75
## 19                                  2.55                                   2.04
## 35                                  1.79                                   1.03
## 20                                  2.46                                   1.70
## 8                                   4.00                                   3.60
## 7                                   4.33                                   4.22
## 14                                  3.68                                   3.59
## 34                                  1.55                                   0.83
## 3                                   4.26                                   4.46
## 1                                   3.18                                   2.58
## 18                                  4.21                                   3.85
## 31                                  2.56                                   1.70
## 38                                  3.08                                   2.16
## 37                                  2.78                                   2.15
## 25                                  3.59                                   2.98
## 29                                  4.39                                   4.04
## 4                                   3.96                                   3.49
## 11                                  3.98                                   3.32
## 30                                  2.96                                   2.24
## 32                                  4.17                                   3.56
## 5                                   3.03                                   2.11
## 9                                   2.59                                   1.57
## 36                                  4.39                                   3.58
## 16                                  3.10                                   2.18
## 22                                  3.51                                   2.40
## 6                                   4.31                                   3.54
## 21                                  4.17                                   2.73

10.Student Teacher Ratio

Student-teacher ratio or student-faculty ratio is the number of students who attend a school or university divided by the number of teachers in the institution. For example, a student-teacher ratio of 10:1 indicates that there are 10 students for every one teacher. Factors that can affect the relationship between student-teacher ratio and class size include the number of teachers with non-teaching duties, the number of classes per teacher, and the number of teachers per class. Classes with too many students are often disrupting to education. Also, too many students in a class results in a diverse field of students, with varying degrees of learning ability. Consequently, the class will spend time for less academic students to assimilate the information, when that time could be better spent progressing through the curriculum. Hence to promote learning a lower student teacher ratio should be maintained. This socio-economic parameter tells us about the state of education, schooling infrastructure, availablity of teachers, etc. The scale here is in ratio.

Now to analyse this socio-economic parameter we will plot the bar graph, code for which is given below.

ggplot(bihar, aes(State...District,group = 1,y = Student.Teacher.Ratio,label= Student.Teacher.Ratio)) + 
    geom_bar(stat = "identity", aes(y = Student.Teacher.Ratio, colour = "Student.Teacher.Ratio")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + geom_hline(aes(yintercept=69.21, colour = 'Mean Student.Teacher.Ratio')) + ggtitle("Student Teacher Ratio") + 
    theme(plot.title = element_text(face="bold")) + theme(legend.position = "bottom", legend.direction = "vertical")+ geom_text(size = 3, colour = 'red', vjust=2)

alt text

As we can observe the student teacher ratio is poor in the entire state, especially in Jamui district. Now to order different districts of Bihar on the basis of student teacher ratio we run following code. Here the districts are ranked as per their respective student teacher ratio. [Lower is better].

student <- bihar[ order(bihar[,25]), ]
student[,c(1,25)]
##      State...District Student.Teacher.Ratio
## 18         Lakhisarai                 34.25
## 15            Katihar                 43.45
## 32              Saran                 44.10
## 38           Vaishali                 46.39
## 2              Araria                 50.39
## 5           Begusarai                 50.66
## 22        Muzaffarpur                 51.85
## 7             Bhojpur                 53.50
## 8               Buxar                 56.11
## 34            Sheohar                 56.11
## 37             Supaul                 56.37
## 23            Nalanda                 56.85
## 26              Patna                 59.34
## 13          Jehanabad                 60.33
## 33         Sheikhpura                 63.62
## 14    Kaimur (Bhabua)                 66.76
## 21             Munger                 67.16
## 35          Sitamarhi                 68.15
## 1               Bihar                 69.21
## 28             Purnia                 69.25
## 10               Gaya                 72.27
## 24             Nawada                 78.22
## 27    Purba Champaran                 80.63
## 20          Madhubani                 82.86
## 29             Rohtas                 84.11
## 4               Banka                 86.66
## 16           Khagaria                 88.76
## 31         Samastipur                 88.86
## 36              Siwan                 92.21
## 25 Pashchim Champaran                 93.60
## 6           Bhagalpur                 93.80
## 9           Darbhanga                109.26
## 19          Madhepura                109.35
## 30            Saharsa                109.35
## 11          Gopalganj                116.75
## 3          Aurangabad                126.66
## 17         Kishanganj                135.53
## 12              Jamui                367.09

Result

The given problem statement was solved and a comprehensive report is presented above on 10 socio-economic parameters.

*The image & excel file(s) used are also attached. This file needs to be placed in the same folder with the “bihar_sample.xlsx” for it to work.


Refrences

  1. data.gov.in
  2. Rajya Swastha samiti,Bihar
  3. Census India
  4. Directorate Of Economics and Statistics, Govt. Of Bihar